Data & AI Delivery Lead Enterprise Asset Management (EAM)

Job Description

Randstad is seeking a high-caliber Data & AI Delivery Lead - Enterprise Asset Management (EAM) to drive the end-to-end execution of advanced data, analytics, and AI/GenAI solutions for a major rail and transit client in the Washington, DC area. Operating at the intersection of business strategy, program delivery, and hands-on technical execution, this role serves as the primary technical leader owning the Databricks Lakehouse architecture to modernize infrastructure asset management, condition monitoring, and long-term capital planning. As a core delivery anchor within the Infrastructure EAM workstream, you will lead cross-functional teams to transform traditional, fixed-interval maintenance into predictive, risk-based interventions that minimize operational risk and lower project costs.

Key Responsibilities

Technical Leadership & Solution Delivery: Oversee end-to-end delivery of analytics, machine learning, predictive modeling, and GenAI use cases on the enterprise Databricks platform.

Scalable Data Pipeline Design: Build and optimize robust pipelines using Databricks Workflows and the Medallion Architecture to ingest, process, and curate complex sensor feeds, inspection records, maintenance histories, and operational/financial datasets.

Predictive & Lifecycle Modeling: Guide the development of predictive health models, failure probability algorithms, and Remaining Useful Life (RUL) indicators, alongside financial lifecycle cost models to support risk-based capital allocation.

Governance & Platform Optimization: Implement enterprise data governance, lineage, and security standards using Unity Catalog while evaluating and integrating modern Databricks features (e.g., Delta Live Tables, MLflow, Vector Search).

Stakeholder & Domain Alignment: Partner with engineering, reliability, and operations teams to deploy interactive dashboards and risk-scoring frameworks aligned with industry standards (e.g., ISO 55000) and regulatory requirements.

Program Execution: Bridge executive business strategy and technical execution during high-demand project phases, serving as a dedicated expert backfill to drive productivity and maintain project momentum.

Required Qualifications

7+ years of progressive experience in data engineering, advanced data analytics, or asset analytics roles.

3+ years of project or program management experience leading complex enterprise data initiatives or asset management solutions.

Hands-on Databricks Command: Proven practical experience with the Databricks Lakehouse ecosystem, including Medallion Architecture, Unity Catalog, and modern AI/ML tooling.

Domain Knowledge: Deep familiarity with reliability engineering, condition monitoring, predictive maintenance techniques, or enterprise asset management concepts.

Location: Based in or willing to work with a client in the Washington, DC area.

Preferred Qualifications

Direct experience with rail infrastructure, transit networks, or linear assets.

Databricks Certified Data Engineer (Professional) or Databricks Certified Machine Learning (Associate/Professional).